Deep learning for medical image analysis / edited by S. Kevin Zhou, Hayit Greenspan, Dinggang Shen.

Front Cover; Deep Learning for Medical Image Analysis; Copyright; Contents; Contributors; About the Editors; Foreword; Part 1 Introduction; 1 An Introduction to Neural Networks and Deep Learning; 1.1 Introduction; 1.2 Feed-Forward Neural Networks; 1.2.1 Perceptron; 1.2.2 Multi-Layer Neural Network; 1.2.3 Learning in Feed-Forward Neural Networks; 1.3 Convolutional Neural Networks; 1.3.1 Convolution and Pooling Layer; 1.3.2 Computing Gradients; 1.4 Deep Models; 1.4.1 Vanishing Gradient Problem; 1.4.2 Deep Neural Networks; 1.4.3 Deep Generative Models; 1.5 Tricks for Better Learning.

1.5.1 Rectified Linear Unit (ReLU)1.5.2 Dropout; 1.5.3 Batch Normalization; 1.6 Open-Source Tools for Deep Learning; References; Notes; 2 An Introduction to Deep Convolutional Neural Nets for Computer Vision; 2.1 Introduction; 2.2 Convolutional Neural Networks; 2.2.1 Building Blocks of CNNs; 2.2.2 Depth; 2.2.3 Learning Algorithm; 2.2.4 Tricks to Increase Performance; 2.2.5 Putting It All Together: AlexNet; 2.2.6 Using Pre-Trained CNNs; 2.2.7 Improving AlexNet; 2.3 CNN Flavors; 2.3.1 Region-Based CNNs; 2.3.2 Fully Convolutional Networks; 2.3.3 Multi-Modal Networks; 2.3.4 CNNs with RNNs.

2.3.5 Hybrid Learning Methods2.4 Software for Deep Learning; References; Part 2 Medical Image Detection and Recognition; 3 Efficient Medical Image Parsing; 3.1 Introduction; 3.2 Background and Motivation; 3.2.1 Object Localization and Segmentation: Challenges; 3.3 Methodology; 3.3.1 Problem Formulation; 3.3.2 Sparse Adaptive Deep Neural Networks; 3.3.3 Marginal Space Deep Learning; 3.3.4 An Artificial Agent for Image Parsing; 3.4 Experiments; 3.4.1 Anatomy Detection and Segmentation in 3D; 3.4.2 Landmark Detection in 2D and 3D; 3.5 Conclusion; Disclaimer; References.

4 Multi-Instance Multi-Stage Deep Learning for Medical Image Recognition4.1 Introduction; 4.2 Related Work; 4.3 Methodology; 4.3.1 Problem Statement and Framework Overview; 4.3.2 Learning Stage I: Multi-Instance CNN Pre-Train; 4.3.3 Learning Stage II: CNN Boosting; 4.3.4 Run-Time Classification; 4.4 Results; 4.4.1 Image Classification on Synthetic Data; 4.4.2 Body-Part Recognition on CT Slices; 4.5 Discussion and Future Work; References; 5 Automatic Interpretation of Carotid Intima-Media Thickness Videos Using Convolutional Neural Networks; 5.1 Introduction; 5.2 Related Work.

5.3 CIMT Protocol5.4 Method; 5.4.1 Convolutional Neural Networks (CNNs); 5.4.2 Frame Selection; 5.4.3 ROI Localization; 5.4.4 Intima-Media Thickness Measurement; 5.5 Experiments; 5.5.1 Pre- and Post-Processing for Frame Selection; 5.5.2 Constrained ROI Localization; 5.5.3 Intima-Media Thickness Measurement; 5.5.4 End-to-End CIMT Measurement; 5.6 Discussion; 5.7 Conclusion; Acknowledgement; References; Notes; 6 Deep Cascaded Networks for Sparsely Distributed Object Detection from Medical Images; 6.1 Introduction; 6.2 Method; 6.2.1 Coarse Retrieval Model; 6.2.2 Fine Discrimination Model.

Includes bibliographical references and index.

Online resource; title from PDF title page (ScienceDirect, viewed February 2, 2017).

"Deep learning is providing exciting solutions for medical image analysis problems and is seen as a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component have been applied to medical image detection, segmentation and registration, and computer-aided analysis, using a wide variety of application areas. Deep Learning for Medical Image Analysis is a great learning resource for academic and industry researchers in medical imaging analysis, and for graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis"-- provided by publisher.